Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations80695
Missing cells161390
Missing cells (%)4.9%
Total size in memory25.2 MiB
Average record size in memory328.0 B

Variable types

Text9
Unsupported2
Numeric30

Alerts

nand_type has 80695 (100.0%) missing valuesMissing
workload_type has 80695 (100.0%) missing valuesMissing
host_read_cmds_per_power_cycle has unique valuesUnique
nand_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
workload_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
unsafe_shutdowns has 15960 (19.8%) zerosZeros
media_errors has 19756 (24.5%) zerosZeros
error_information_log_entries has 19756 (24.5%) zerosZeros
bad_block_count_grown has 16518 (20.5%) zerosZeros
pcie_correctable_errors has 2697 (3.3%) zerosZeros
pcie_uncorrectable_errors has 45161 (56.0%) zerosZeros

Reproduction

Analysis started2025-12-28 15:23:38.205294
Analysis finished2025-12-28 15:23:40.580812
Duration2.38 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct80481
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:40.950714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2017375
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80267 ?
Unique (%)99.5%

Sample

1st row2025-07-10 04:56:06+00:00
2nd row2025-02-16 20:51:01+00:00
3rd row2025-04-29 10:40:28+00:00
4th row2025-04-16 10:19:45+00:00
5th row2025-04-07 03:26:20+00:00
ValueCountFrequency (%)
2025-05-25545
 
0.3%
2025-03-15540
 
0.3%
2025-06-11527
 
0.3%
2025-04-04523
 
0.3%
2025-03-19521
 
0.3%
2025-07-06518
 
0.3%
2025-02-21514
 
0.3%
2025-05-09512
 
0.3%
2025-05-30510
 
0.3%
2025-06-27510
 
0.3%
Other values (52659)156170
96.8%
2025-12-28T08:23:41.489066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0599962
29.7%
2272274
13.5%
:242085
12.0%
-161390
 
8.0%
5153592
 
7.6%
1123449
 
6.1%
80695
 
4.0%
+80695
 
4.0%
379451
 
3.9%
471905
 
3.6%
Other values (4)151877
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2017375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0599962
29.7%
2272274
13.5%
:242085
12.0%
-161390
 
8.0%
5153592
 
7.6%
1123449
 
6.1%
80695
 
4.0%
+80695
 
4.0%
379451
 
3.9%
471905
 
3.6%
Other values (4)151877
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2017375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0599962
29.7%
2272274
13.5%
:242085
12.0%
-161390
 
8.0%
5153592
 
7.6%
1123449
 
6.1%
80695
 
4.0%
+80695
 
4.0%
379451
 
3.9%
471905
 
3.6%
Other values (4)151877
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2017375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0599962
29.7%
2272274
13.5%
:242085
12.0%
-161390
 
8.0%
5153592
 
7.6%
1123449
 
6.1%
80695
 
4.0%
+80695
 
4.0%
379451
 
3.9%
471905
 
3.6%
Other values (4)151877
 
7.5%

ff
Text

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:41.672666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.375649049
Min length3

Characters and Unicode

Total characters272398
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE1.s
2nd rowE1.s
3rd rowE1.s
4th rowE1.s
5th rowE1.s
ValueCountFrequency (%)
u.216798
20.8%
e3.s16793
20.8%
m.216793
20.8%
u.316791
20.8%
e1.s13520
16.8%
2025-12-28T08:23:41.986230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.80695
29.6%
233591
12.3%
U33589
12.3%
333584
12.3%
E30313
 
11.1%
s30313
 
11.1%
M16793
 
6.2%
113520
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)272398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.80695
29.6%
233591
12.3%
U33589
12.3%
333584
12.3%
E30313
 
11.1%
s30313
 
11.1%
M16793
 
6.2%
113520
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)272398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.80695
29.6%
233591
12.3%
U33589
12.3%
333584
12.3%
E30313
 
11.1%
s30313
 
11.1%
M16793
 
6.2%
113520
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)272398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.80695
29.6%
233591
12.3%
U33589
12.3%
333584
12.3%
E30313
 
11.1%
s30313
 
11.1%
M16793
 
6.2%
113520
 
5.0%
Distinct34673
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:42.400032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters484170
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10798 ?
Unique (%)13.4%

Sample

1st rowPM4614
2nd rowPM2124
3rd rowNV8878
4th rowPM1594
5th rowNV3105
ValueCountFrequency (%)
nv509610
 
< 0.1%
xg033910
 
< 0.1%
nv26869
 
< 0.1%
xg38399
 
< 0.1%
nv20989
 
< 0.1%
mt38949
 
< 0.1%
pm19399
 
< 0.1%
nv95399
 
< 0.1%
pm51759
 
< 0.1%
nv85908
 
< 0.1%
Other values (34663)80604
99.9%
2025-12-28T08:23:42.970896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M40463
 
8.4%
832566
 
6.7%
632428
 
6.7%
032357
 
6.7%
432351
 
6.7%
232288
 
6.7%
932243
 
6.7%
332214
 
6.7%
132137
 
6.6%
732137
 
6.6%
Other values (7)152986
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)484170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M40463
 
8.4%
832566
 
6.7%
632428
 
6.7%
032357
 
6.7%
432351
 
6.7%
232288
 
6.7%
932243
 
6.7%
332214
 
6.7%
132137
 
6.6%
732137
 
6.6%
Other values (7)152986
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)484170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M40463
 
8.4%
832566
 
6.7%
632428
 
6.7%
032357
 
6.7%
432351
 
6.7%
232288
 
6.7%
932243
 
6.7%
332214
 
6.7%
132137
 
6.6%
732137
 
6.6%
Other values (7)152986
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)484170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M40463
 
8.4%
832566
 
6.7%
632428
 
6.7%
032357
 
6.7%
432351
 
6.7%
232288
 
6.7%
932243
 
6.7%
332214
 
6.7%
132137
 
6.6%
732137
 
6.6%
Other values (7)152986
31.6%
Distinct77171
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:43.336546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters645560
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73751 ?
Unique (%)91.4%

Sample

1st rowF65874.1
2nd rowF72199.6
3rd rowF73321.4
4th rowF32025.4
5th rowF24230.9
ValueCountFrequency (%)
f54993.24
 
< 0.1%
f60399.63
 
< 0.1%
f21659.03
 
< 0.1%
f90756.93
 
< 0.1%
f18559.23
 
< 0.1%
f16021.43
 
< 0.1%
f15294.13
 
< 0.1%
f14159.03
 
< 0.1%
f95393.93
 
< 0.1%
f55244.63
 
< 0.1%
Other values (77161)80664
> 99.9%
2025-12-28T08:23:43.951726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F80695
12.5%
.80695
12.5%
949884
7.7%
149689
7.7%
649330
7.6%
249269
7.6%
749205
7.6%
449172
7.6%
849105
7.6%
549075
7.6%
Other values (2)89441
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)645560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F80695
12.5%
.80695
12.5%
949884
7.7%
149689
7.7%
649330
7.6%
249269
7.6%
749205
7.6%
449172
7.6%
849105
7.6%
549075
7.6%
Other values (2)89441
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)645560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F80695
12.5%
.80695
12.5%
949884
7.7%
149689
7.7%
649330
7.6%
249269
7.6%
749205
7.6%
449172
7.6%
849105
7.6%
549075
7.6%
Other values (2)89441
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)645560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F80695
12.5%
.80695
12.5%
949884
7.7%
149689
7.7%
649330
7.6%
249269
7.6%
749205
7.6%
449172
7.6%
849105
7.6%
549075
7.6%
Other values (2)89441
13.9%

nand_type
Unsupported

Missing  Rejected  Unsupported 

Missing80695
Missing (%)100.0%
Memory size630.6 KiB

nvme_capacity_tb
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.30017969
Minimum4
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:44.094439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median15
Q325
95-th percentile256
Maximum256
Range252
Interquartile range (IQR)21

Descriptive statistics

Standard deviation72.37602719
Coefficient of variation (CV)1.597698457
Kurtosis3.394836901
Mean45.30017969
Median Absolute Deviation (MAD)11
Skewness2.157508892
Sum3655498
Variance5238.289312
MonotonicityNot monotonic
2025-12-28T08:23:44.216196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
420995
26.0%
2513996
17.3%
813993
17.3%
1512118
15.0%
2567000
 
8.7%
607000
 
8.7%
1285593
 
6.9%
ValueCountFrequency (%)
420995
26.0%
813993
17.3%
1512118
15.0%
2513996
17.3%
607000
 
8.7%
ValueCountFrequency (%)
2567000
8.7%
1285593
 
6.9%
607000
8.7%
2513996
17.3%
1512118
15.0%

overprovisioning_ratio
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.998240288
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:44.338244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.427139888
Coefficient of variation (CV)0.2855284671
Kurtosis-1.243738119
Mean4.998240288
Median Absolute Deviation (MAD)1
Skewness-0.002713160839
Sum403333
Variance2.036728259
MonotonicityNot monotonic
2025-12-28T08:23:44.460164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
616237
20.1%
516138
20.0%
416070
19.9%
315876
19.7%
715765
19.5%
2319
 
0.4%
8290
 
0.4%
ValueCountFrequency (%)
2319
 
0.4%
315876
19.7%
416070
19.9%
516138
20.0%
616237
20.1%
ValueCountFrequency (%)
8290
 
0.4%
715765
19.5%
616237
20.1%
516138
20.0%
416070
19.9%

composite_temperature_c
Real number (ℝ)

Distinct239
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.61214945
Minimum26.5
Maximum50.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:44.603636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum26.5
5-th percentile28.6
Q132
median35.6
Q338.9
95-th percentile43.3
Maximum50.4
Range23.9
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation4.547614642
Coefficient of variation (CV)0.1276984038
Kurtosis-0.4761945253
Mean35.61214945
Median Absolute Deviation (MAD)3.4
Skewness0.2244368698
Sum2873722.4
Variance20.68079893
MonotonicityNot monotonic
2025-12-28T08:23:44.775975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.6681
 
0.8%
39.2670
 
0.8%
36.8669
 
0.8%
37.4667
 
0.8%
38.4644
 
0.8%
38.3640
 
0.8%
37.3638
 
0.8%
38.2634
 
0.8%
38.1633
 
0.8%
38.9632
 
0.8%
Other values (229)74187
91.9%
ValueCountFrequency (%)
26.564
0.1%
26.6119
0.1%
26.7128
0.2%
26.8114
0.1%
26.9128
0.2%
ValueCountFrequency (%)
50.41
 
< 0.1%
50.23
< 0.1%
50.13
< 0.1%
506
< 0.1%
49.93
< 0.1%

data_units_read
Real number (ℝ)

Distinct80689
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249565128.7
Minimum10003113
Maximum1599014608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:44.938510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10003113
5-th percentile27603954.2
Q187145162
median158684397
Q3321783203
95-th percentile751601159.6
Maximum1599014608
Range1589011495
Interquartile range (IQR)234638041

Descriptive statistics

Standard deviation258959917.7
Coefficient of variation (CV)1.037644639
Kurtosis5.918747399
Mean249565128.7
Median Absolute Deviation (MAD)89818220
Skewness2.219482436
Sum2.013865806 × 1013
Variance6.7060239 × 1016
MonotonicityNot monotonic
2025-12-28T08:23:45.120327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
516486072
 
< 0.1%
1989307582
 
< 0.1%
1481397022
 
< 0.1%
1643104852
 
< 0.1%
3936568682
 
< 0.1%
777875542
 
< 0.1%
2149082441
 
< 0.1%
3382137801
 
< 0.1%
6970731641
 
< 0.1%
1717325341
 
< 0.1%
Other values (80679)80679
> 99.9%
ValueCountFrequency (%)
100031131
< 0.1%
100112991
< 0.1%
100161771
< 0.1%
100212581
< 0.1%
100251701
< 0.1%
ValueCountFrequency (%)
15990146081
< 0.1%
15988750881
< 0.1%
15981246641
< 0.1%
15981049681
< 0.1%
15980211121
< 0.1%

data_units_written
Real number (ℝ)

Distinct80681
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187350991.9
Minimum8000435
Maximum1199759576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:45.289680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum8000435
5-th percentile20899215.9
Q165548514
median119433183
Q3240275054
95-th percentile563543931.2
Maximum1199759576
Range1191759141
Interquartile range (IQR)174726540

Descriptive statistics

Standard deviation194526708.6
Coefficient of variation (CV)1.038300927
Kurtosis5.945208745
Mean187350991.9
Median Absolute Deviation (MAD)67456965
Skewness2.228832483
Sum1.511828829 × 1013
Variance3.784064035 × 1016
MonotonicityNot monotonic
2025-12-28T08:23:45.474656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
712169282
 
< 0.1%
921152972
 
< 0.1%
1217640262
 
< 0.1%
306693642
 
< 0.1%
3634946682
 
< 0.1%
1353227162
 
< 0.1%
1918275322
 
< 0.1%
1301945442
 
< 0.1%
324766052
 
< 0.1%
896391222
 
< 0.1%
Other values (80671)80675
> 99.9%
ValueCountFrequency (%)
80004351
< 0.1%
80044271
< 0.1%
80196541
< 0.1%
80213381
< 0.1%
80245941
< 0.1%
ValueCountFrequency (%)
11997595761
< 0.1%
11992971921
< 0.1%
11992848561
< 0.1%
11988909201
< 0.1%
11988857121
< 0.1%

host_read_commands
Real number (ℝ)

Distinct80693
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1246034882
Minimum500030920
Maximum1999999687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:45.637443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum500030920
5-th percentile571975364.3
Q1868162447.5
median1245063246
Q31622487502
95-th percentile1923303880
Maximum1999999687
Range1499968767
Interquartile range (IQR)754325054

Descriptive statistics

Standard deviation433762830.6
Coefficient of variation (CV)0.3481145167
Kurtosis-1.205421922
Mean1246034882
Median Absolute Deviation (MAD)377175577
Skewness0.006263539017
Sum1.005487848 × 1014
Variance1.881501932 × 1017
MonotonicityNot monotonic
2025-12-28T08:23:45.806676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19932143662
 
< 0.1%
12504298402
 
< 0.1%
5862525621
 
< 0.1%
13815187531
 
< 0.1%
9501134531
 
< 0.1%
19255557011
 
< 0.1%
16921864181
 
< 0.1%
9023955831
 
< 0.1%
13135562831
 
< 0.1%
12422495401
 
< 0.1%
Other values (80683)80683
> 99.9%
ValueCountFrequency (%)
5000309201
< 0.1%
5000490911
< 0.1%
5000562451
< 0.1%
5000776731
< 0.1%
5000846401
< 0.1%
ValueCountFrequency (%)
19999996871
< 0.1%
19999837131
< 0.1%
19999681281
< 0.1%
19999433461
< 0.1%
19999159371
< 0.1%

host_write_commands
Real number (ℝ)

Distinct80687
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean934949085.2
Minimum300186431
Maximum1795878100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:45.975932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum300186431
5-th percentile421822735.7
Q1644227090
median921816923
Q31201198786
95-th percentile1520619660
Maximum1795878100
Range1495691669
Interquartile range (IQR)556971696

Descriptive statistics

Standard deviation344895728.9
Coefficient of variation (CV)0.3688925251
Kurtosis-0.9126537442
Mean934949085.2
Median Absolute Deviation (MAD)278539643
Skewness0.1961328158
Sum7.544571643 × 1013
Variance1.189530638 × 1017
MonotonicityNot monotonic
2025-12-28T08:23:46.154335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6003586972
 
< 0.1%
12290997432
 
< 0.1%
13272801382
 
< 0.1%
13280281772
 
< 0.1%
11131540392
 
< 0.1%
7376074142
 
< 0.1%
10469844432
 
< 0.1%
11500064612
 
< 0.1%
9755699151
 
< 0.1%
9451799731
 
< 0.1%
Other values (80677)80677
> 99.9%
ValueCountFrequency (%)
3001864311
< 0.1%
3018106391
< 0.1%
3025206731
< 0.1%
3029802841
< 0.1%
3030553941
< 0.1%
ValueCountFrequency (%)
17958781001
< 0.1%
17924839591
< 0.1%
17924819121
< 0.1%
17924778381
< 0.1%
17882481991
< 0.1%

avg_queue_depth
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.25958238
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:46.276824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q316
95-th percentile32
Maximum32
Range31
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.14143295
Coefficient of variation (CV)0.90879384
Kurtosis-0.7308144263
Mean12.25958238
Median Absolute Deviation (MAD)7
Skewness0.8349025881
Sum989287
Variance124.1315281
MonotonicityNot monotonic
2025-12-28T08:23:46.408369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
3216328
20.2%
816117
20.0%
116099
20.0%
1616096
19.9%
416055
19.9%
ValueCountFrequency (%)
116099
20.0%
416055
19.9%
816117
20.0%
1616096
19.9%
3216328
20.2%
ValueCountFrequency (%)
3216328
20.2%
1616096
19.9%
816117
20.0%
416055
19.9%
116099
20.0%

iops
Real number (ℝ)

Distinct58717
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64777.8414
Minimum6000
Maximum167997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:46.562010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6000
5-th percentile13262.7
Q132339
median56090
Q391109
95-th percentile148924.3
Maximum167997
Range161997
Interquartile range (IQR)58770

Descriptive statistics

Standard deviation41019.43966
Coefficient of variation (CV)0.6332325803
Kurtosis-0.3450646381
Mean64777.8414
Median Absolute Deviation (MAD)27041
Skewness0.7443155934
Sum5227247912
Variance1682594430
MonotonicityNot monotonic
2025-12-28T08:23:46.740420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
438788
 
< 0.1%
706677
 
< 0.1%
314706
 
< 0.1%
411886
 
< 0.1%
675576
 
< 0.1%
414666
 
< 0.1%
273216
 
< 0.1%
647406
 
< 0.1%
611546
 
< 0.1%
211846
 
< 0.1%
Other values (58707)80632
99.9%
ValueCountFrequency (%)
60001
< 0.1%
60011
< 0.1%
60102
< 0.1%
60121
< 0.1%
60182
< 0.1%
ValueCountFrequency (%)
1679972
< 0.1%
1679932
< 0.1%
1679841
< 0.1%
1679832
< 0.1%
1679811
< 0.1%

bandwidth_read_gbps
Real number (ℝ)

Distinct134
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.645743726
Minimum0.6
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:46.909741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.96
Q11.98
median3.1
Q35.1
95-th percentile8.1
Maximum9
Range8.4
Interquartile range (IQR)3.12

Descriptive statistics

Standard deviation2.147326636
Coefficient of variation (CV)0.5889954965
Kurtosis-0.3256297043
Mean3.645743726
Median Absolute Deviation (MAD)1.36
Skewness0.7852748322
Sum294193.29
Variance4.611011683
MonotonicityNot monotonic
2025-12-28T08:23:47.063388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.71620
 
2.0%
2.11617
 
2.0%
31614
 
2.0%
1.81308
 
1.6%
3.61305
 
1.6%
1.51281
 
1.6%
3.91042
 
1.3%
4.5969
 
1.2%
1.2969
 
1.2%
2.4961
 
1.2%
Other values (124)68009
84.3%
ValueCountFrequency (%)
0.6291
0.4%
0.66654
0.8%
0.72680
0.8%
0.78677
0.8%
0.84669
0.8%
ValueCountFrequency (%)
9334
0.4%
8.85690
0.9%
8.7635
0.8%
8.55689
0.9%
8.4621
0.8%

bandwidth_write_gbps
Real number (ℝ)

Distinct109
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.129245554
Minimum0.6
Maximum7.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:47.210561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.9
Q11.8
median2.64
Q34.3
95-th percentile6.75
Maximum7.5
Range6.9
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.770786736
Coefficient of variation (CV)0.5658829598
Kurtosis-0.3728585557
Mean3.129245554
Median Absolute Deviation (MAD)1.11
Skewness0.7673354772
Sum252514.47
Variance3.135685665
MonotonicityNot monotonic
2025-12-28T08:23:47.379875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.72053
 
2.5%
2.11992
 
2.5%
1.81650
 
2.0%
1.51579
 
2.0%
31575
 
2.0%
3.91248
 
1.5%
3.61236
 
1.5%
2.41227
 
1.5%
4.51173
 
1.5%
1.21162
 
1.4%
Other values (99)65800
81.5%
ValueCountFrequency (%)
0.6351
0.4%
0.66812
1.0%
0.72827
1.0%
0.78823
1.0%
0.84775
1.0%
ValueCountFrequency (%)
7.5386
0.5%
7.35809
1.0%
7.2829
1.0%
7.05834
1.0%
6.9829
1.0%

io_completion_time_ms
Real number (ℝ)

Distinct1284
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2742023124
Minimum0.035
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:47.687584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.0665
Q10.15
median0.2535
Q30.3679
95-th percentile0.5772
Maximum0.65
Range0.615
Interquartile range (IQR)0.2179

Descriptive statistics

Standard deviation0.1533456979
Coefficient of variation (CV)0.5592429057
Kurtosis-0.5084336803
Mean0.2742023124
Median Absolute Deviation (MAD)0.1066
Skewness0.557436264
Sum22126.7556
Variance0.02351490308
MonotonicityNot monotonic
2025-12-28T08:23:47.847790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.182209
 
0.3%
0.091173
 
0.2%
0.1911168
 
0.2%
0.3276162
 
0.2%
0.1638153
 
0.2%
0.3094151
 
0.2%
0.2821147
 
0.2%
0.3458145
 
0.2%
0.2366142
 
0.2%
0.3185141
 
0.2%
Other values (1274)79104
98.0%
ValueCountFrequency (%)
0.03525
 
< 0.1%
0.035771
0.1%
0.036477
0.1%
0.037173
0.1%
0.037872
0.1%
ValueCountFrequency (%)
0.6542
0.1%
0.648758
0.1%
0.647471
0.1%
0.646171
0.1%
0.644882
0.1%

power_cycles
Real number (ℝ)

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.55448293
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:48.012421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median26
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.44415989
Coefficient of variation (CV)0.5652299805
Kurtosis-1.200324896
Mean25.55448293
Median Absolute Deviation (MAD)12
Skewness-0.006630758669
Sum2062119
Variance208.6337549
MonotonicityNot monotonic
2025-12-28T08:23:48.184923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61704
 
2.1%
431700
 
2.1%
151694
 
2.1%
331689
 
2.1%
461685
 
2.1%
161672
 
2.1%
311672
 
2.1%
21672
 
2.1%
281665
 
2.1%
221654
 
2.0%
Other values (40)63888
79.2%
ValueCountFrequency (%)
11645
2.0%
21672
2.1%
31539
1.9%
41604
2.0%
51628
2.0%
ValueCountFrequency (%)
501645
2.0%
491608
2.0%
481619
2.0%
471577
2.0%
461685
2.1%

power_on_hours
Real number (ℝ)

Distinct7001
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4503.868691
Minimum1000
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:48.355115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1350.7
Q12757
median4511
Q36253
95-th percentile7647
Maximum8000
Range7000
Interquartile range (IQR)3496

Descriptive statistics

Standard deviation2018.831811
Coefficient of variation (CV)0.4482439319
Kurtosis-1.199043088
Mean4503.868691
Median Absolute Deviation (MAD)1748
Skewness-0.007660651471
Sum363439684
Variance4075681.88
MonotonicityNot monotonic
2025-12-28T08:23:48.527130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195127
 
< 0.1%
333225
 
< 0.1%
564624
 
< 0.1%
430324
 
< 0.1%
106723
 
< 0.1%
155823
 
< 0.1%
739323
 
< 0.1%
681523
 
< 0.1%
687023
 
< 0.1%
280723
 
< 0.1%
Other values (6991)80457
99.7%
ValueCountFrequency (%)
10009
< 0.1%
100116
< 0.1%
10028
< 0.1%
100310
< 0.1%
10048
< 0.1%
ValueCountFrequency (%)
80007
< 0.1%
79998
< 0.1%
79988
< 0.1%
79979
< 0.1%
799615
< 0.1%

controller_busy_time
Real number (ℝ)

Distinct1479
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean628.0750852
Minimum100
Maximum1595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:48.696357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile155
Q1378
median627
Q3853
95-th percentile1167
Maximum1595
Range1495
Interquartile range (IQR)475

Descriptive statistics

Standard deviation308.7172123
Coefficient of variation (CV)0.4915291493
Kurtosis-0.4405743
Mean628.0750852
Median Absolute Deviation (MAD)236
Skewness0.2892288559
Sum50682519
Variance95306.3172
MonotonicityNot monotonic
2025-12-28T08:23:48.859109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
719120
 
0.1%
925119
 
0.1%
954114
 
0.1%
922111
 
0.1%
813110
 
0.1%
645110
 
0.1%
859110
 
0.1%
647109
 
0.1%
972108
 
0.1%
803108
 
0.1%
Other values (1469)79576
98.6%
ValueCountFrequency (%)
10081
0.1%
10164
0.1%
10287
0.1%
10375
0.1%
10478
0.1%
ValueCountFrequency (%)
15951
< 0.1%
15911
< 0.1%
15891
< 0.1%
15851
< 0.1%
15831
< 0.1%

percentage_used
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.496908111
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:49.012821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.872724559
Coefficient of variation (CV)0.522607346
Kurtosis-1.22205813
Mean5.496908111
Median Absolute Deviation (MAD)2
Skewness0.0002653344312
Sum443573
Variance8.252546392
MonotonicityNot monotonic
2025-12-28T08:23:49.113120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
68222
10.2%
18141
10.1%
48130
10.1%
108099
10.0%
78073
10.0%
88068
10.0%
38058
10.0%
28013
9.9%
97970
9.9%
57921
9.8%
ValueCountFrequency (%)
18141
10.1%
28013
9.9%
38058
10.0%
48130
10.1%
57921
9.8%
ValueCountFrequency (%)
108099
10.0%
97970
9.9%
88068
10.0%
78073
10.0%
68222
10.2%

wear_level_avg
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.998438565
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:49.229008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.414133833
Coefficient of variation (CV)0.471623414
Kurtosis-1.300271426
Mean2.998438565
Median Absolute Deviation (MAD)1
Skewness0.002418593385
Sum241959
Variance1.999774497
MonotonicityNot monotonic
2025-12-28T08:23:49.360460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
216213
20.1%
116137
20.0%
516124
20.0%
416113
20.0%
316108
20.0%
ValueCountFrequency (%)
116137
20.0%
216213
20.1%
316108
20.0%
416113
20.0%
516124
20.0%
ValueCountFrequency (%)
516124
20.0%
416113
20.0%
316108
20.0%
216213
20.1%
116137
20.0%

wear_level_max
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.503017535
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:49.498538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.800517847
Coefficient of variation (CV)0.3998469543
Kurtosis-0.6949389702
Mean4.503017535
Median Absolute Deviation (MAD)1
Skewness0.0005471740875
Sum363371
Variance3.241864516
MonotonicityNot monotonic
2025-12-28T08:23:49.614497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
516134
20.0%
416107
20.0%
312164
15.1%
612138
15.0%
78103
10.0%
28064
10.0%
84017
 
5.0%
13968
 
4.9%
ValueCountFrequency (%)
13968
 
4.9%
28064
10.0%
312164
15.1%
416107
20.0%
516134
20.0%
ValueCountFrequency (%)
84017
 
5.0%
78103
10.0%
612138
15.0%
516134
20.0%
416107
20.0%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.89828366
Minimum86
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:49.761572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile87
Q190
median93
Q396
95-th percentile98
Maximum99
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.482816541
Coefficient of variation (CV)0.03749064465
Kurtosis-1.092156206
Mean92.89828366
Median Absolute Deviation (MAD)3
Skewness-0.119969445
Sum7496427
Variance12.13001106
MonotonicityNot monotonic
2025-12-28T08:23:49.893150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
977823
9.7%
987676
9.5%
937305
9.1%
907076
8.8%
966973
8.6%
946941
8.6%
896774
8.4%
916722
8.3%
956309
7.8%
926303
7.8%
Other values (4)10793
13.4%
ValueCountFrequency (%)
861899
 
2.4%
873293
4.1%
884622
5.7%
896774
8.4%
907076
8.8%
ValueCountFrequency (%)
99979
 
1.2%
987676
9.5%
977823
9.7%
966973
8.6%
956309
7.8%

unsafe_shutdowns
Real number (ℝ)

Zeros 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.536191833
Minimum0
Maximum4
Zeros15960
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:50.015526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.082249936
Coefficient of variation (CV)0.7045018157
Kurtosis-0.7534778626
Mean1.536191833
Median Absolute Deviation (MAD)1
Skewness0.2124194873
Sum123963
Variance1.171264924
MonotonicityNot monotonic
2025-12-28T08:23:50.131386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
224379
30.2%
124211
30.0%
015960
19.8%
313586
16.8%
42559
 
3.2%
ValueCountFrequency (%)
015960
19.8%
124211
30.0%
224379
30.2%
313586
16.8%
42559
 
3.2%
ValueCountFrequency (%)
42559
 
3.2%
313586
16.8%
224379
30.2%
124211
30.0%
015960
19.8%

background_scrub_time_pct
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5495730838
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:50.278488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.6
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2630970372
Coefficient of variation (CV)0.4787298449
Kurtosis-1.148277344
Mean0.5495730838
Median Absolute Deviation (MAD)0.2
Skewness-0.0009229586557
Sum44347.8
Variance0.06922005097
MonotonicityNot monotonic
2025-12-28T08:23:50.378724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.79125
11.3%
0.39093
11.3%
0.89085
11.3%
0.29007
11.2%
0.58893
11.0%
0.68874
11.0%
0.48833
10.9%
0.98797
10.9%
0.14496
5.6%
14492
5.6%
ValueCountFrequency (%)
0.14496
5.6%
0.29007
11.2%
0.39093
11.3%
0.48833
10.9%
0.58893
11.0%
ValueCountFrequency (%)
14492
5.6%
0.98797
10.9%
0.89085
11.3%
0.79125
11.3%
0.68874
11.0%

gc_active_time_pct
Real number (ℝ)

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.501898507
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:50.532397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q12.3
median3.5
Q34.7
95-th percentile5.7
Maximum6
Range5
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation1.443012403
Coefficient of variation (CV)0.4120657408
Kurtosis-1.198924885
Mean3.501898507
Median Absolute Deviation (MAD)1.2
Skewness-0.002330726888
Sum282585.7
Variance2.082284794
MonotonicityNot monotonic
2025-12-28T08:23:50.701660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.51707
 
2.1%
4.61684
 
2.1%
4.21659
 
2.1%
3.71652
 
2.0%
2.61652
 
2.0%
5.11651
 
2.0%
2.11648
 
2.0%
1.91647
 
2.0%
4.41641
 
2.0%
1.71641
 
2.0%
Other values (41)64113
79.5%
ValueCountFrequency (%)
1815
1.0%
1.11573
1.9%
1.21633
2.0%
1.31610
2.0%
1.41595
2.0%
ValueCountFrequency (%)
6797
1.0%
5.91613
2.0%
5.81611
2.0%
5.71625
2.0%
5.61596
2.0%

media_errors
Real number (ℝ)

Zeros 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.818092819
Minimum0
Maximum75
Zeros19756
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:50.864477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile25
Maximum75
Range75
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.12010114
Coefficient of variation (CV)1.484300876
Kurtosis10.18439238
Mean6.818092819
Median Absolute Deviation (MAD)4
Skewness2.947047088
Sum550186
Variance102.4164471
MonotonicityNot monotonic
2025-12-28T08:23:51.018219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
019756
24.5%
211012
13.6%
59500
11.8%
108206
10.2%
46431
 
8.0%
15017
 
6.2%
154030
 
5.0%
63291
 
4.1%
32298
 
2.8%
201969
 
2.4%
Other values (25)9185
11.4%
ValueCountFrequency (%)
019756
24.5%
15017
 
6.2%
211012
13.6%
32298
 
2.8%
46431
 
8.0%
ValueCountFrequency (%)
754
 
< 0.1%
7034
 
< 0.1%
65100
 
0.1%
60274
0.3%
55505
0.6%

error_information_log_entries
Real number (ℝ)

Zeros 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.747642357
Minimum0
Maximum35
Zeros19756
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:51.165392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile16
Maximum35
Range35
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.995327071
Coefficient of variation (CV)1.332925235
Kurtosis5.714369767
Mean3.747642357
Median Absolute Deviation (MAD)2
Skewness2.310613577
Sum302416
Variance24.95329255
MonotonicityNot monotonic
2025-12-28T08:23:51.319036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
019756
24.5%
112994
16.1%
211911
14.8%
49075
11.2%
37659
 
9.5%
63311
 
4.1%
53129
 
3.9%
72116
 
2.6%
91299
 
1.6%
81165
 
1.4%
Other values (26)8280
10.3%
ValueCountFrequency (%)
019756
24.5%
112994
16.1%
211911
14.8%
37659
 
9.5%
49075
11.2%
ValueCountFrequency (%)
351
 
< 0.1%
341
 
< 0.1%
332
 
< 0.1%
3210
< 0.1%
3116
< 0.1%

bad_block_count_grown
Real number (ℝ)

Zeros 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.93679906
Minimum0
Maximum55
Zeros16518
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:51.466196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q315
95-th percentile40
Maximum55
Range55
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.08500251
Coefficient of variation (CV)1.10498533
Kurtosis1.658558573
Mean10.93679906
Median Absolute Deviation (MAD)6
Skewness1.463754669
Sum882545
Variance146.0472857
MonotonicityNot monotonic
2025-12-28T08:23:51.597707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
016518
20.5%
106958
 
8.6%
55858
 
7.3%
44961
 
6.1%
24826
 
6.0%
204394
 
5.4%
64350
 
5.4%
153819
 
4.7%
83810
 
4.7%
253546
 
4.4%
Other values (16)21655
26.8%
ValueCountFrequency (%)
016518
20.5%
12053
 
2.5%
24826
 
6.0%
32188
 
2.7%
44961
 
6.1%
ValueCountFrequency (%)
55442
 
0.5%
50823
 
1.0%
451215
1.5%
401720
2.1%
352132
2.6%

pcie_correctable_errors
Real number (ℝ)

Zeros 

Distinct116
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.89839519
Minimum0
Maximum260
Zeros2697
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:51.751446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median42
Q394
95-th percentile215
Maximum260
Range260
Interquartile range (IQR)79

Descriptive statistics

Standard deviation66.77786398
Coefficient of variation (CV)0.9981982945
Kurtosis0.3371549633
Mean66.89839519
Median Absolute Deviation (MAD)33
Skewness1.180889651
Sum5398366
Variance4459.283118
MonotonicityNot monotonic
2025-12-28T08:23:51.920805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102880
 
3.6%
02697
 
3.3%
52196
 
2.7%
202183
 
2.7%
41857
 
2.3%
21841
 
2.3%
251804
 
2.2%
151716
 
2.1%
81433
 
1.8%
61410
 
1.7%
Other values (106)60678
75.2%
ValueCountFrequency (%)
02697
3.3%
1743
 
0.9%
21841
2.3%
3799
 
1.0%
41857
2.3%
ValueCountFrequency (%)
26011
 
< 0.1%
25552
 
0.1%
250232
 
0.3%
245575
0.7%
240614
0.8%

pcie_uncorrectable_errors
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.381870004
Minimum0
Maximum10
Zeros45161
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:52.052344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.996451594
Coefficient of variation (CV)1.444746313
Kurtosis1.00057462
Mean1.381870004
Median Absolute Deviation (MAD)0
Skewness1.359683183
Sum111510
Variance3.985818968
MonotonicityNot monotonic
2025-12-28T08:23:52.168263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
045161
56.0%
514925
 
18.5%
211386
 
14.1%
18459
 
10.5%
10433
 
0.5%
4331
 
0.4%
ValueCountFrequency (%)
045161
56.0%
18459
 
10.5%
211386
 
14.1%
4331
 
0.4%
514925
 
18.5%
ValueCountFrequency (%)
10433
 
0.5%
514925
18.5%
4331
 
0.4%
211386
14.1%
18459
10.5%

workload_type
Unsupported

Missing  Rejected  Unsupported 

Missing80695
Missing (%)100.0%
Memory size630.6 KiB

queue_depth
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.25958238
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:52.268537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q316
95-th percentile32
Maximum32
Range31
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.14143295
Coefficient of variation (CV)0.90879384
Kurtosis-0.7308144263
Mean12.25958238
Median Absolute Deviation (MAD)7
Skewness0.8349025881
Sum989287
Variance124.1315281
MonotonicityNot monotonic
2025-12-28T08:23:52.384458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
3216328
20.2%
816117
20.0%
116099
20.0%
1616096
19.9%
416055
19.9%
ValueCountFrequency (%)
116099
20.0%
416055
19.9%
816117
20.0%
1616096
19.9%
3216328
20.2%
ValueCountFrequency (%)
3216328
20.2%
1616096
19.9%
816117
20.0%
416055
19.9%
116099
20.0%

workload_block_size_kb
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.100625813
Minimum0.5
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:52.647907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q14
median4
Q38
95-th percentile16
Maximum16
Range15.5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.744267546
Coefficient of variation (CV)0.8089804613
Kurtosis-1.123802819
Mean7.100625813
Median Absolute Deviation (MAD)3.5
Skewness0.5026117029
Sum572985
Variance32.99660964
MonotonicityNot monotonic
2025-12-28T08:23:52.761639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
420446
25.3%
820313
25.2%
0.520018
24.8%
1619918
24.7%
ValueCountFrequency (%)
0.520018
24.8%
420446
25.3%
820313
25.2%
1619918
24.7%
ValueCountFrequency (%)
1619918
24.7%
820313
25.2%
420446
25.3%
0.520018
24.8%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:52.934729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.985277898
Min length3

Characters and Unicode

Total characters482982
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInspur
2nd rowHPE
3rd rowLenovo
4th rowDell
5th rowInspur
ValueCountFrequency (%)
dell13661
16.9%
fujitsu13615
16.9%
lenovo13512
16.7%
inspur13392
16.6%
hpe13363
16.6%
supermicro13152
16.3%
2025-12-28T08:23:53.280601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u53774
 
11.1%
e40325
 
8.3%
o40176
 
8.3%
r39696
 
8.2%
l27322
 
5.7%
s27007
 
5.6%
n26904
 
5.6%
i26767
 
5.5%
p26544
 
5.5%
D13661
 
2.8%
Other values (12)160806
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)482982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u53774
 
11.1%
e40325
 
8.3%
o40176
 
8.3%
r39696
 
8.2%
l27322
 
5.7%
s27007
 
5.6%
n26904
 
5.6%
i26767
 
5.5%
p26544
 
5.5%
D13661
 
2.8%
Other values (12)160806
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)482982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u53774
 
11.1%
e40325
 
8.3%
o40176
 
8.3%
r39696
 
8.2%
l27322
 
5.7%
s27007
 
5.6%
n26904
 
5.6%
i26767
 
5.5%
p26544
 
5.5%
D13661
 
2.8%
Other values (12)160806
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)482982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u53774
 
11.1%
e40325
 
8.3%
o40176
 
8.3%
r39696
 
8.2%
l27322
 
5.7%
s27007
 
5.6%
n26904
 
5.6%
i26767
 
5.5%
p26544
 
5.5%
D13661
 
2.8%
Other values (12)160806
33.3%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:53.472612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length28
Median length24
Mean length17.81147531
Min length11

Characters and Unicode

Total characters1437297
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInspur ThinkSystem SR650
2nd rowHPE NF5280M6
3rd rowLenovo R740
4th rowDell ThinkSystem SR650
5th rowInspur NF5280M6
ValueCountFrequency (%)
thinksystem26940
12.5%
sr65026940
12.5%
ucs20349
9.5%
c24020349
9.5%
dell13661
 
6.3%
fujitsu13615
 
6.3%
lenovo13512
 
6.3%
r74013512
 
6.3%
inspur13392
 
6.2%
hpe13363
 
6.2%
Other values (4)39654
18.4%
2025-12-28T08:23:53.826211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134592
 
9.4%
S87381
 
6.1%
080695
 
5.6%
e67265
 
4.7%
n60452
 
4.2%
i60315
 
4.2%
s53947
 
3.8%
u53774
 
3.7%
t47163
 
3.3%
o46784
 
3.3%
Other values (31)744929
51.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1437297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
134592
 
9.4%
S87381
 
6.1%
080695
 
5.6%
e67265
 
4.7%
n60452
 
4.2%
i60315
 
4.2%
s53947
 
3.8%
u53774
 
3.7%
t47163
 
3.3%
o46784
 
3.3%
Other values (31)744929
51.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1437297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
134592
 
9.4%
S87381
 
6.1%
080695
 
5.6%
e67265
 
4.7%
n60452
 
4.2%
i60315
 
4.2%
s53947
 
3.8%
u53774
 
3.7%
t47163
 
3.3%
o46784
 
3.3%
Other values (31)744929
51.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1437297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
134592
 
9.4%
S87381
 
6.1%
080695
 
5.6%
e67265
 
4.7%
n60452
 
4.2%
i60315
 
4.2%
s53947
 
3.8%
u53774
 
3.7%
t47163
 
3.3%
o46784
 
3.3%
Other values (31)744929
51.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:53.977602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.99592292
Min length3

Characters and Unicode

Total characters322451
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntel
2nd rowIntel
3rd rowAMD
4th rowAMD
5th rowAMD
ValueCountFrequency (%)
amd40512
50.2%
intel40183
49.8%
2025-12-28T08:23:54.289625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A40512
12.6%
M40512
12.6%
D40512
12.6%
I40183
12.5%
n40183
12.5%
t40183
12.5%
e40183
12.5%
l40183
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)322451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A40512
12.6%
M40512
12.6%
D40512
12.6%
I40183
12.5%
n40183
12.5%
t40183
12.5%
e40183
12.5%
l40183
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)322451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A40512
12.6%
M40512
12.6%
D40512
12.6%
I40183
12.5%
n40183
12.5%
t40183
12.5%
e40183
12.5%
l40183
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)322451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A40512
12.6%
M40512
12.6%
D40512
12.6%
I40183
12.5%
n40183
12.5%
t40183
12.5%
e40183
12.5%
l40183
12.5%
Distinct9997
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:54.546568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters968340
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowABCD8560EFGH
2nd rowABCD6974EFGH
3rd rowABCD0599EFGH
4th rowABCD1234EFGH
5th rowABCD8462EFGH
ValueCountFrequency (%)
abcd5750efgh20
 
< 0.1%
abcd0300efgh20
 
< 0.1%
abcd9599efgh19
 
< 0.1%
abcd0769efgh19
 
< 0.1%
abcd7689efgh19
 
< 0.1%
abcd2591efgh18
 
< 0.1%
abcd7537efgh18
 
< 0.1%
abcd2739efgh18
 
< 0.1%
abcd3464efgh18
 
< 0.1%
abcd6165efgh18
 
< 0.1%
Other values (9987)80508
99.8%
2025-12-28T08:23:54.953209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A80695
 
8.3%
E80695
 
8.3%
H80695
 
8.3%
B80695
 
8.3%
F80695
 
8.3%
G80695
 
8.3%
D80695
 
8.3%
C80695
 
8.3%
132589
 
3.4%
032549
 
3.4%
Other values (8)257642
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)968340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A80695
 
8.3%
E80695
 
8.3%
H80695
 
8.3%
B80695
 
8.3%
F80695
 
8.3%
G80695
 
8.3%
D80695
 
8.3%
C80695
 
8.3%
132589
 
3.4%
032549
 
3.4%
Other values (8)257642
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)968340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A80695
 
8.3%
E80695
 
8.3%
H80695
 
8.3%
B80695
 
8.3%
F80695
 
8.3%
G80695
 
8.3%
D80695
 
8.3%
C80695
 
8.3%
132589
 
3.4%
032549
 
3.4%
Other values (8)257642
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)968340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A80695
 
8.3%
E80695
 
8.3%
H80695
 
8.3%
B80695
 
8.3%
F80695
 
8.3%
G80695
 
8.3%
D80695
 
8.3%
C80695
 
8.3%
132589
 
3.4%
032549
 
3.4%
Other values (8)257642
26.6%
Distinct1000
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:55.268644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters726255
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD3V941VAB
2nd rowD3V926VAB
3rd rowD3V502VAB
4th rowD3V489VAB
5th rowD3V158VAB
ValueCountFrequency (%)
d3v159vab110
 
0.1%
d3v546vab109
 
0.1%
d3v345vab107
 
0.1%
d3v690vab107
 
0.1%
d3v091vab106
 
0.1%
d3v006vab103
 
0.1%
d3v868vab103
 
0.1%
d3v300vab102
 
0.1%
d3v011vab102
 
0.1%
d3v710vab102
 
0.1%
Other values (990)79644
98.7%
2025-12-28T08:23:55.695931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V161390
22.2%
3104772
14.4%
D80695
11.1%
A80695
11.1%
B80695
11.1%
024414
 
3.4%
424342
 
3.4%
624328
 
3.3%
224298
 
3.3%
524215
 
3.3%
Other values (4)96411
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)726255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V161390
22.2%
3104772
14.4%
D80695
11.1%
A80695
11.1%
B80695
11.1%
024414
 
3.4%
424342
 
3.4%
624328
 
3.3%
224298
 
3.3%
524215
 
3.3%
Other values (4)96411
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)726255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V161390
22.2%
3104772
14.4%
D80695
11.1%
A80695
11.1%
B80695
11.1%
024414
 
3.4%
424342
 
3.4%
624328
 
3.3%
224298
 
3.3%
524215
 
3.3%
Other values (4)96411
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)726255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V161390
22.2%
3104772
14.4%
D80695
11.1%
A80695
11.1%
B80695
11.1%
024414
 
3.4%
424342
 
3.4%
624328
 
3.3%
224298
 
3.3%
524215
 
3.3%
Other values (4)96411
13.3%

host_read_cmds_per_power_cycle
Real number (ℝ)

Unique 

Distinct80695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112411164.2
Minimum10008157.08
Maximum1998259716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.6 KiB
2025-12-28T08:23:55.886283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10008157.08
5-th percentile16734785.95
Q131155289.26
median48776362.82
Q395789241.93
95-th percentile420146894
Maximum1998259716
Range1988251559
Interquartile range (IQR)64633952.67

Descriptive statistics

Standard deviation210162436
Coefficient of variation (CV)1.869586864
Kurtosis30.81229882
Mean112411164.2
Median Absolute Deviation (MAD)23426848.54
Skewness5.05698897
Sum9.071018896 × 1012
Variance4.416824952 × 1016
MonotonicityNot monotonic
2025-12-28T08:23:56.101430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72039225.241
 
< 0.1%
13958394.331
 
< 0.1%
39471964.371
 
< 0.1%
22095661.71
 
< 0.1%
40969270.231
 
< 0.1%
84609320.91
 
< 0.1%
9023955831
 
< 0.1%
50521395.51
 
< 0.1%
3105623851
 
< 0.1%
102651066.61
 
< 0.1%
Other values (80685)80685
> 99.9%
ValueCountFrequency (%)
10008157.081
< 0.1%
10011541.721
< 0.1%
10067713.741
< 0.1%
10076980.081
< 0.1%
10080072.81
< 0.1%
ValueCountFrequency (%)
19982597161
< 0.1%
19965522711
< 0.1%
19965116481
< 0.1%
19960108691
< 0.1%
19943214171
< 0.1%